A neural network lab experiment

Neural networks are becoming widely used in complex control problems. Many academic exercises approach neural network applications using only software simulations; however, simulations alone do not give students a full appreciation of the power and complexity of neural network-based controls. This paper describes a laboratory experiment that uses a temperature and airflow process simulator to demonstrate neural network control applications. The simulator is fundamentally a temperature controller in which large-scale changes in forced airflow produce significant changes in heat load. The initial labs use PID control techniques to solve the temperature control problem and to demonstrate the problem that PID controllers have with large disturbances. The following labs address the same problem using a neural network control strategy. An actual neural network controller is built and used to perform the same temperature control as the classical PID system. Capabilities and drawbacks of neural network control are demonstrated.